湖南农业大学学报（自然科学版）2023，Vol.49Issue(6)：743-747,5.DOI:10.13331/j.cnki.jhau.2023.06.017
基于LSTM的柑橘幼苗蒸发量预测
The prediction of evaporation for citrus seedlings based on LSTM
摘要
Abstract
In this study,citrus seedlings were selected to estimate the predictions of evaporation.The air relative humidity and temperature were collected by sensors and mass method was used to collect the mass change of crops in real time as crop evaporation.The substrate relative humidity,temperature and EC value were used as environmental factors.With environmental factors as model input and crop evaporation as model output,a long short-term memory neural network(LSTM)prediction model was constructed.The optimized model structure and training parameters included 1 hidden layer of the LSTM model,120 hidden layer nodes,128 iteration samples,and 175 training iterations.The activation function of the network is tanh function,the learning rate was 0.001,and the time step was 72.The coefficient of determination(R2),root mean square error(RMSE)and mean absolute error(MAE)of LSTM prediction model were 0.993 9,0.015 5 g and 0.011 3 g,respectively.Compared with the prediction effect of recurrent neural network(RNN)and gated cycle unit(GRU),the predicted evaporation value from LSTM prediction model was closer to the real evaporation value,and the relative error range of prediction results had the smallest fluctuation,RMSE and MAE were the smallest,and R2 was the largest,indicating that the prediction effect of LSTM prediction model was the best among these three models.关键词
柑橘幼苗/蒸发量/环境因子/长短期记忆神经网络(LSTM)Key words
citrus seedlings/evaporation/environmental factor/long short-term memory neural network(LSTM)分类
农业科技引用本文复制引用
代秋芳,熊诗路,李震,宋淑然,陈梓蔚,王元..基于LSTM的柑橘幼苗蒸发量预测[J].湖南农业大学学报（自然科学版）,2023,49(6):743-747,5.基金项目
国家自然科学基金项目(31971797) （31971797）
广东省现代农业产业技术体系创新团队建设项目(2022KJ108) （2022KJ108）
财政部和农业农村部国家现代农业产业技术体系(CARS-26) （CARS-26）
